SpaCET.deconvolution.matched.scRNAseq: Deconvolve ST data set with matched scRNAseq data

In data2intelligence/SpaCE: Spatial Cellular Estimator for Tumors

Deconvolve ST data set with matched scRNAseq data

Description

Estimate the fraction of cell lineage and sub lineage.

Usage

SpaCET.deconvolution.matched.scRNAseq(
  SpaCET_obj,
  sc_includeMalignant = TRUE,
  cancerType,
  sc_counts,
  sc_annotation,
  sc_lineageTree,
  sc_nCellEachLineage = 100,
  coreNo = 8
)

Arguments

SpaCET_obj	An SpaCET object.
sc_includeMalignant	Logical. Indicate whether the single cell data includes malignant cells. If no, please input a cancer type and then SpaCET will predict the malignant cell fraction based on its build-in reference.
cancerType	Cancer type of the current tumor ST dataset.
sc_counts	Single cell count matrix with gene name (row) x cell ID (column).
sc_annotation	Single cell annotation matrix. This matrix should include two columns, i,e., cellID and cellType. Each row represents a single cell.
sc_lineageTree	Cell lineage tree. This should be organized by using a list, and the name of each element are major lineages while the value of elements are the corresponding sublineages. If a major lineage does not have any sublineages, the value of this major lineage should be itself.
sc_nCellEachLineage	Cell count each lineage. Default: 100. If a cell type is comprised of >100 cells, only 100 cells per cell identity are randomly selected to generate cell type reference.
coreNo	Core number.

Value

An SpaCET object

Examples

SpaCET_obj <- SpaCET.deconvolution.matched.scRNAseq(SpaCET_obj, sc_counts, sc_annotation, sc_lineageTree)

data2intelligence/SpaCE documentation built on Nov. 15, 2024, 12:03 a.m.